The distinction of risk vs uncertainty as made by Knight has important implications for policy selection. Assuming the former when the latter is relevant can lead to wrong decisions. With the aid of a stylized model that describes a bank's decision on how to allocate loans, the authors discuss policy insights for decision making under Knightian uncertainty. They use the info-gap robust satisficing approach to derive a trade-off between confidence and performance (analogous to confidence intervals in the Bayesian approach but without assignment of probabilities). The authors show that this trade off can be interpreted as a cost of robustness. They show that the robustness analysis can lead to a reversal of policy preference from the putative optimum. The authors then compare this approach to the min-max method which is the other main non-probabilistic approach available in the literature. They also consider conceptual proxies for robustness and demonstrate their use in qualitative analysis of financial architecture and monetary policy.